Multi-object tracking has been studied for decades. However, when it comes to tracking pedestrians in extremely crowded scenes, we are limited to only few works. This is an important problem which gives rise to several challenges. Pre-trained object detectors fail to localize targets in crowded sequences. This consequently limits the use of data-association based multi-target tracking methods which rely on the outcome of an object detector. Additionally, the small apparent target size makes it challenging to extract features to discriminate targets from their surroundings. Finally, the large number of targets greatly increases computational complexity which in turn makes it hard to extend existing multi-target tracking approaches to high-density crowd scenarios. In this paper, we propose a tracker that addresses the aforementioned problems and is capable of tracking hundreds of people efficiently. We formulate online crowd tracking as Binary Quadratic Programing. Our formulation employs target's individual information in the form of appearance and motion as well as contextual cues in the form of neighborhood motion, spatial proximity and grouping, and solves detection and data association simultaneously. In order to solve the proposed quadratic optimization efficiently, where state-of art commercial quadratic programing solvers fail to find the solution in a reasonable amount of time, we propose to use the most recent version of the Modified Frank Wolfe algorithm, which takes advantage of SWAP-steps to speed up the optimization. We show that the proposed formulation can track hundreds of targets efficiently and improves state-of-art results by significant margins on eleven challenging high density crowd sequences.

中文翻译：

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在线跟踪极端拥挤场景中数百人的二进制二次编程

多目标跟踪已经研究了数十年。但是，当涉及到在非常拥挤的场景中跟踪行人时，我们仅限于很少的作品。这是一个重要的问题，引起了一些挑战。预训练的物体检测器无法按拥挤的序列定位目标。因此，这限制了基于数据关联的多目标跟踪方法的使用，该方法依赖于对象检测器的结果。此外，较小的目标表面尺寸使其难以提取特征以将目标与周围环境区分开。最后，大量目标极大地增加了计算复杂度，从而使现有的多目标跟踪方法难以扩展到高密度人群场景。在本文中，我们提出了一种跟踪器，该跟踪器可以解决上述问题，并且能够有效地跟踪数百人。我们将在线人群跟踪公式化为二进制二次编程。我们的公式采用目标对象的外观和运动形式的个人信息，以及邻域运动，空间邻近性和分组形式的上下文提示，并同时解决检测和数据关联。为了有效地解决所提出的二次优化问题，而最新的商业二次编程求解器却无法在合理的时间内找到解决方案，因此，我们建议使用最新版本的Modified Frank Wolfe算法，该算法利用了SWAP步骤可加快优化速度。